Fetch Sitewide Analytics
Start of analysis period as an ISO 8601 datetime string (e.g. '2025-01-01T00:00:00.000-05:00'). Defaults to the start of the day 30 days ago, in the store's timezone. Note the default differs per sitewide endpoint, so pass an explicit start if you need a specific window.
^(?:(?:\d\d[2468][048]|\d\d[13579][26]|\d\d0[48]|[02468][048]00|[13579][26]00)-02-29|\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\d|30)|(?:02)-(?:0[1-9]|1\d|2[0-8])))T(?:(?:[01]\d|2[0-3]):[0-5]\d(?::[0-5]\d(?:\.\d+)?)?(?:Z|([+-](?:[01]\d|2[0-3]):[0-5]\d)))$End of analysis period as an ISO 8601 datetime string. Defaults to now.
^(?:(?:\d\d[2468][048]|\d\d[13579][26]|\d\d0[48]|[02468][048]00|[13579][26]00)-02-29|\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\d|30)|(?:02)-(?:0[1-9]|1\d|2[0-8])))T(?:(?:[01]\d|2[0-3]):[0-5]\d(?::[0-5]\d(?:\.\d+)?)?(?:Z|([+-](?:[01]\d|2[0-3]):[0-5]\d)))$Optional dimension that pivots the funnel's step-1 entry-point nodes. This does NOT filter the dataset — it only changes which buckets are rendered at step 1 (Mobile vs. Desktop, New vs. Returning, channel breakdown, source-site breakdown). To narrow the dataset to a single segment, use the filters object instead. Supported values: 'device_type' (Mobile vs Desktop), 'visitor_type' (New vs Returning), 'source_channel' (Paid Social, Paid Search, Direct, ...), 'source_site' (referrer host buckets). Any other value (typo, unsupported dimension, wrong type) silently falls back to 'device_type'. Mirrors the Customer Journey chart's audience selector in the Intelligems dashboard. NOTE: this parameter was named audience in earlier v25-10-beta builds (renamed to disambiguate it from the audience segmentation param on the snapshot/timeseries endpoints). A legacy audience key sent to this endpoint is now ignored — update integrations to entryPoint.
device_typePossible values: OK
OK
Start of analysis period as an ISO 8601 datetime string (e.g. '2025-01-01T00:00:00.000-05:00'). Defaults to the start of the week containing the date 12 weeks ago, in the store's timezone (weeks start Monday), so the default start is week-aligned rather than exactly 84 days back. Note the default differs per sitewide endpoint, so pass an explicit start if you need a specific window.
^(?:(?:\d\d[2468][048]|\d\d[13579][26]|\d\d0[48]|[02468][048]00|[13579][26]00)-02-29|\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\d|30)|(?:02)-(?:0[1-9]|1\d|2[0-8])))T(?:(?:[01]\d|2[0-3]):[0-5]\d(?::[0-5]\d(?:\.\d+)?)?(?:Z|([+-](?:[01]\d|2[0-3]):[0-5]\d)))$End of analysis period as an ISO 8601 datetime string. Defaults to now.
^(?:(?:\d\d[2468][048]|\d\d[13579][26]|\d\d0[48]|[02468][048]00|[13579][26]00)-02-29|\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\d|30)|(?:02)-(?:0[1-9]|1\d|2[0-8])))T(?:(?:[01]\d|2[0-3]):[0-5]\d(?::[0-5]\d(?:\.\d+)?)?(?:Z|([+-](?:[01]\d|2[0-3]):[0-5]\d)))$OK
OK
Start of analysis period as an ISO 8601 datetime string (e.g. '2025-01-01T00:00:00.000-05:00'). Defaults to the start of the week containing the date one week ago, in the store's timezone (weeks start Monday) — so the default window spans 7 to 13 days depending on the current weekday, not exactly 7. Note the default differs per sitewide endpoint, so pass an explicit start if you need a specific window.
^(?:(?:\d\d[2468][048]|\d\d[13579][26]|\d\d0[48]|[02468][048]00|[13579][26]00)-02-29|\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\d|30)|(?:02)-(?:0[1-9]|1\d|2[0-8])))T(?:(?:[01]\d|2[0-3]):[0-5]\d(?::[0-5]\d(?:\.\d+)?)?(?:Z|([+-](?:[01]\d|2[0-3]):[0-5]\d)))$End of analysis period as an ISO 8601 datetime string. Defaults to now.
^(?:(?:\d\d[2468][048]|\d\d[13579][26]|\d\d0[48]|[02468][048]00|[13579][26]00)-02-29|\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\d|30)|(?:02)-(?:0[1-9]|1\d|2[0-8])))T(?:(?:[01]\d|2[0-3]):[0-5]\d(?::[0-5]\d(?:\.\d+)?)?(?:Z|([+-](?:[01]\d|2[0-3]):[0-5]\d)))$Optional feature selector object. Omit it or use { "name": "performance" } for sitewide KPI snapshots, { "name": "audience", "audience": "device_type" } for audience segment snapshots, { "name": "order" } for order-focused metrics, or { "name": "conversion" } for conversion funnel metrics.
OK
OK
Start of analysis period as an ISO 8601 datetime string (e.g. '2025-01-01T00:00:00.000-05:00'). Defaults to the start of the week containing the date 12 weeks ago, in the store's timezone (weeks start Monday), so the default start is week-aligned rather than exactly 84 days back. Note the default differs per sitewide endpoint, so pass an explicit start if you need a specific window.
^(?:(?:\d\d[2468][048]|\d\d[13579][26]|\d\d0[48]|[02468][048]00|[13579][26]00)-02-29|\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\d|30)|(?:02)-(?:0[1-9]|1\d|2[0-8])))T(?:(?:[01]\d|2[0-3]):[0-5]\d(?::[0-5]\d(?:\.\d+)?)?(?:Z|([+-](?:[01]\d|2[0-3]):[0-5]\d)))$End of analysis period as an ISO 8601 datetime string. Defaults to now.
^(?:(?:\d\d[2468][048]|\d\d[13579][26]|\d\d0[48]|[02468][048]00|[13579][26]00)-02-29|\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\d|30)|(?:02)-(?:0[1-9]|1\d|2[0-8])))T(?:(?:[01]\d|2[0-3]):[0-5]\d(?::[0-5]\d(?:\.\d+)?)?(?:Z|([+-](?:[01]\d|2[0-3]):[0-5]\d)))$Optional metric-set selector. Object form, e.g. { "name": "performance" }. Values: 'performance' (headline KPI trend), 'order' (order composition and per-unit economics), 'conversion' (funnel step rates), or { name: 'audience', audience: '' } for a per-segment breakdown. Omit to receive every metric this endpoint produces. NOTE: the fields behind each set are specific to this endpoint and differ from the sitewide snapshot endpoint's set of the same name, because the two run different underlying datasets.
Time bucket granularity. Supported values: day, week, month. Defaults to week.
weekPossible values: OK
OK
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